Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-19T01:49:02.018Z Has data issue: false hasContentIssue false
  • English
  • Français

An Ordinal Model of Risk Based on Mariner's Judgement

Published online by Cambridge University Press:  14 September 2016

Adan Lopez-Santander  and
Jonathan Lawry
Adan Lopez-Santander*
Affiliation:
(Department of Engineering Mathematics, University of Bristol)
Jonathan Lawry
Affiliation:
(Department of Engineering Mathematics, University of Bristol)
*
(E-mail: enals@bristol.ac.uk)
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes a statistical method for learning and estimating the risk posed by other craft in the vicinity of a vessel and an overview of its possible spatial application, simulating how professional mariners perceive and assess such risk and using navigational data obtained from a standard integrated bridge. We propose a non-linear model for risk estimation which attempts to capture mariners' judgement. Questionnaire data has been collected that captures and quantifies mariners’ judgements of risk for craft in the vicinity, where each craft is described by measurements that can be obtained easily from the data already present in the ship's navigational equipment. The dataset has then been used for analysis, training and validating Ordered Probit models in order to obtain a computationally efficient data driven model for estimating the risk probability vector posed by other craft. Finally, we discuss how this risk model can be incorporated into decision making and path finding algorithms.

Keywords

Craft's riskRisk estimationMariner's knowledgeCollision avoidance
Type
Review Article
Information
The Journal of Navigation , Volume 70 , Issue 2 , March 2017 , pp. 309 - 324
Copyright
Copyright © The Royal Institute of Navigation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ai, C. and Norton, E.C. (2003). Interaction terms in logit and probit models. Economics Letters, 80, 123–129.CrossRefGoogle Scholar
Aitchison, J. and Silvey, S.D. (1957). The Generalization of Probit Analysis to the Case of Multiple Responses. Biometrika, 44, 131–140.Google Scholar
Andreas Toscher, M.J. (2009). The Big Chaos Solution to the Netflix Grand Prize. AT&T Labs.Google Scholar
Bartus, T. (2005). Estimation of marginal effects using margeff. Stata Journal, 5, 309–329.Google Scholar
Becker, W.E. and Kennedy, P.E. (1992). A Graphical Exposition of the Ordered Probit. Econometric Theory, 8, 127–131.Google Scholar
Belkhouche, F. and Bendjilali, B. (2013). Dynamic collision risk modeling under uncertainty. Robotica, 31, 525–537.Google Scholar
Bukhari, A.C., Tusseyeva, I., Lee, B.-G. and Kim, Y.-G. (2013). An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system. Expert Systems with Applications, 40, 1220–1230.CrossRefGoogle Scholar
Chin, H.C. and Debnath, A.K. (2009). Modeling perceived collision risk in port water navigation. Safety Science, 47, 1410–1416.Google Scholar
Curtis, R.G. (1986). A Ship Collision Model for Overtaking. The Journal of the Operational Research Society, 37, 397–406.Google Scholar
Goerlandt, F. and Montewka, J. (2015). Maritime transportation risk analysis: Review and analysis in light of some foundational issues. Reliability Engineering & System Safety, 138, 115–134.CrossRefGoogle Scholar
Goerlandt, F., Montewka, J., Kuzmin, V. and Kujala, P. (2015). A risk-informed ship collision alert system: Framework and application. Safety Science, 77, 182–204.Google Scholar
Greene, W. (2010). Testing hypotheses about interaction terms in nonlinear models. Economics Letters, 107, 291–296.Google Scholar
Hand, D.J. and Yu, K. (2001). Idiot's Bayes: Not So Stupid after All? International Statistical Review / Revue Internationale de Statistique, 69, 385–398.Google Scholar
Hilgert, H. & Baldauf, M. (1997). A common risk model for the assessment of encounter situations on board ships. Deutsche Hydrografische Zeitschrift, 49, 531–542.Google Scholar
International Maritime Organization (IMO). (2004). SOLAS V, Annex 34. Resolution MSC.192(79).Google Scholar
International Maritime Organization (IMO). (2007). Adoption of the Revised Performance Standards for Integrated Navigation Systems (Ins). In: Committee, T. M. S. (ed.). IMO.Google Scholar
International Maritime Organization (IMO). (2013). Ships' Routeing, London, IMO Publishing.Google Scholar
International Maritime Organization (IMO). (1972). International Regulations for Prevention of Collisions at Sea. London: IMO.Google Scholar
Kearon, J. (1977). Computer program for collision avoidance and track keeping. Conference on Mathematics Aspects of Marine Traffic, 229–242.Google Scholar
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on Artificial intelligence – Volume 2. Montreal, Quebec, Canada: Morgan Kaufmann Publishers Inc.Google Scholar
Koren, Y. (2009). The BellKor Solution to the Netflix Grand Prize. Available at: http://netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf. [Accessed 22 July 2016].Google Scholar
Kullback, S. and Leibler, R.A. (1951). On Information and Sufficiency. The Annals of Mathematical Statistics, 22, 79–86.Google Scholar
Lambert, A., Gruyer, D. and Pierre, G.S. (2008). A fast Monte Carlo algorithm for collision probability estimation. Control, Automation, Robotics and Vision, 10th International Conference on, 2008. 406–411.Google Scholar
Lowd, D. and Domingos, P. (2005). Naive bayes models for probability estimation. Proceedings of the Twenty second International Conference on Machine Learning, 529–536.Google Scholar
McKelvey, R.D. and Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. The Journal of Mathematical Sociology, 4, 103–120.Google Scholar
Schwarz, N., Knauper, B., Hippler, Hans-J., Noelle-Neumann, E. and Clark, L. (1991). Rating Scales: Numeric Values May Change the Meaning of Scale Labels. The Public Opinion Quarterly, 55, 570–582.Google Scholar
O'Donnell, C.J. and Connor, D.H. (1996). Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. Accident Analysis &Prevention, 28, 739–753.Google Scholar
Perera, L.P., Carvalho, J.P. and Soares, C.G. (2012). Intelligent Ocean Navigation and Fuzzy-Bayesian Decision/Action Formulation. IEEE Journal of Oceanic Engineering, 37, 204–219.Google Scholar
Piotte, M. and Chabbert, M. (2009). The Pragmatic Theory solution to the Netflix Grand Prize. Pragmatic Theory Inc.Google Scholar
Plamen Angelov, C.D.B., Xideas, Costas, Patchett, Charles, Ansell, Daren, and Michael Everett, G.L. (2008). A Passive Approach to Autonomous Collision Detection and Avoidance in Uninhabited Aerial Systems. Tenth International Conference on Computer Modeling and Simulation.Google Scholar
Preston, C.C. and Colman, A.M. (2000). Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences. Acta Psychol (Amst), 104, 1–15.CrossRefGoogle ScholarPubMed
Rish, I. (2001). An empirical study of the naive Bayes classifier. IJCAI-01 workshop on “Empirical Methods in AI”.Google Scholar
Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6, 461–464.Google Scholar
Simsir, U., Amasyalı, M.F., Bal, M., Çelebi, U.B. and Ertugrul, S. (2014). Decision support system for collision avoidance of vessels. Applied Soft Computing, 25, 369–378.CrossRefGoogle Scholar
Wildt, A.R. and Mazis, M.B. (1978). Determinants of Scale Response: Label versus Position. Journal of Marketing Research, 15, 261–267.Google Scholar